Optical Character Recognition for Telugu Handwritten Characters

Abstract: In the era of digitization, artificial intelligence has revolutionized the way we process and analyze data. However, a significant portion of historical documents and cultural heritage remains in handwritten form, inaccessible to digital technologies. Optical Character Recognition (OCR) emerges as a crucial solution, enabling the conversion of optical text into digital format, thereby making it editable, searchable, and electronically storable. This technology is vital for organizations and individuals dealing with vast amounts of textual information. By training OCR engines on diverse languages, including Telugu, we can tap into the rich cultural heritage of India’s classical languages. Telugu OCR, in particular, facilitates the preservation of hand- written notes, ancient manuscripts, and historical documents, making them accessible to a broader audience. This digital transformation not only preserves cultural heritage but also enables the dissemination of knowledge and ideas to a wider audience, promoting cultural exchange and understanding.

Keywords: Handwritten Telugu Character Recognition, Optical Character Recognition (OCR), Neural Networks, Deep Learning, Image Processing, Pattern Recognition, Machine Learning